Global approximation to arbitrary cost functions: A Bayesian approach with application to US banking

Panayotis Michaelides, Mike Tsionas, Angelos Vouldis and Konstantinos Konstantakis

European Journal of Operational Research, 2015, vol. 241, issue 1, 148-160

Abstract: This paper proposes and estimates a globally flexible functional form for the cost function, which we call Neural Cost Function (NCF). The proposed specification imposes a priori and satisfies globally all the properties that economic theory dictates. The functional form can be estimated easily using Markov Chain Monte Carlo (MCMC) techniques or standard iterative SURE. We use a large panel of U.S. banks to illustrate our approach. The results are consistent with previous knowledge about the sector and in accordance with mathematical production theory.

Keywords: Cost function; Neural networks; Global approximation (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:241:y:2015:i:1:p:148-160

DOI: 10.1016/j.ejor.2014.08.028

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